ST. LOUIS — Big Data in agriculture isn’t really new. The technology used to collect it may be, but common sense still rules its use.

Steve Sonka realized the value of ag data during his childhood on the family farm in Iowa, where he milked cows and fed chickens. He learned early on to reward cows that filled buckets with milk with an extra scoop of grain.

At the same time, some of the 100 laying hens in the chicken house weren’t producing the same as others. But there was no feasible way of adjusting feed for them, since it was impossible to determine which ones were the under-producers. Developing a system to segregate the birds in order to adjust feed would not have been practical.

“Just because technology exists, that doesn’t mean it’s economical to use it,” Sonka said at an ag information conference here. “That should permeate what we understand about technology.”

Sonka left the farm and became an economist, now serving as emeritus chaired professor of agricultural strategy at the University of Illinois. He also has a post at the University of Maryland and is founder of an ag consulting company.

In agriculture and other industries, data quality carries a high cost. But the adoption of digital knowledge and communication offers farmers an opportunity to be more efficient, even with incomplete information.

“Perfect data is very expensive,” Sonka said.

“One of the things that big data does is it lets us make inferences from less-than-perfect data. That’s important, because less-than-perfect data is less expensive.”

Digital technology has dramatically streamlined business the world over, inside and outside agriculture.

One example is the airline reservation system. In the past, a traveler would contact a travel agent who would contact an agent at an airline, who would then enter the data on a computer.

An executive at American Airlines got the idea of having the travel agent type in the information directly, saving time and eventually making many data-entry jobs unnecessary. Now consumers enter the information themselves online.

The advent of digital data in the business world has turned economic models upside-down. It is no longer a zero-sum game. And incomplete information still has value.

“In basic economics, if I give you an ice cream cone, you’re happy, but I’m not. But if I give you a copy of data on a USB device, I’m still happy,” Sonka said.

“… When data is digitized, it becomes free. If you use it for one purpose, it’s free to use for another purpose. But just because we capture data, not always is it economical. We still need to be driven by whether we’re using it to make positive decisions.”

Sonka said analytics is key, and that process is different today and “gives us more power than it used to.”

“We’ve always used the term Garbage In, Garbage Out. That’s not necessarily the case today,” he said.

Sonka has served in a consulting role in Australia, where he has seen the value of data used to increase efficiency.

Ranchers who graze cattle on large paddocks have always used weather observations to make decisions about when to move them off a pasture. Satellite technology has provided more precise information, saving the ranchers tens of thousands of dollars, in some cases.

“They’re using satellite information to understand the vitality of grass,” Sonka said.

“They are making individual animal observations, linking those to the vitality of grass and using that information to decide when to make this important decision — when to move the animals.

“One farmer says it makes him $40,000 a year in net profit, even though he may not have complete information.”

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Data is everywhere; in the webpages you visit, in blog posts you read, on social media platforms you post on, the videos you watch – all of it and more. It would suffice to say that everything you do on the Internet creates data – as does everything you don’t. We generate nearly 2.5 quintillion bytes of information daily, while Google alone is estimated to store over 10 Exabytes of data on a daily basis.

Digital data from all the above sources and more contributes heavily to the massive influx of Big Data and is changing how businesses operate. The quantity and variety of data and the speed at which it is generated and processed each minute makes this phenomenon truly “big”. It is these variables that define Big Data and make it such a dominant factor in manufacturing, sales, marketing, operations, marketing research and business analysis to name a few of its applications. Big Data has led to significant developments in analytics of both text and video, detecting fraud, and predicting consumer trends. However, while solving several problems for both businesses and consumers, Big Data brings with it challenges of its own.

While methods of storing data and analyzing it have advanced considerably in the last 10 years, the fundamental systems based on Big Data have begun to create potential problems. As a result, businesses are left asking some pertinent questions that are relevant to the future of Big Data.

The volume of data out there is massive and what is needed is a system that can sift through it and make sense of it. While many Big Data systems are relatively new, they are becoming redundant or are overloaded, and are often incapable of responding to demands as they emerge and grow.

Currently, all processes relating to Big Data are executed and overseen by humans. However, imagine if complex processes like extricating relevant data and refining it could be delegated to an intelligent machine. Putting other Big Data processes on auto mode and delegating the machine to manage it could make business processes quicker and smarter. Sounds exciting? This day, however, might just arrive earlier than we think.

Big Data and AI almost seem like a match made in digital heaven. While Big Data can expertly sieve out useful information, AI can help businesses look for useful insights from this information. There are several ways AI can do this, such as through automation of business processes and self-learning and optimization of its performance.

However, its most important function is to act as a platform to facilitate interactions between humans and machines. This would be most evident in sales and marketing processes. In addition to a heavy emphasis on CRM and market research, AI and Big Data are set to revolutionize the role of the salesperson in the next few years. Their application of several horizontal technologies allows AI to easily integrate with any function, product, or service. Businesses that have adopted AI-enhanced tools have seen impressive results as to the effectiveness of sales and marketing activities.

Hence, the point to be noted is that Big Data and AI are technologies that are a synergistic match. They have proven their worth in several practical situations as well, generating high ROIs for their users. The stream of data is nowhere close to slowing down and the sheer volume of data to be utilized continues to grow. But eventually, it is the correlation between Big Data systems and AI that will drive critical decision making.

This next wave of the espousal of AI and Big Data and all the scope for innovation it will bring is an exciting proposition for data scientists and analysts. For businesses, it’s a highly promising development. But like any other new technology and process, it needs companies to be prepared, and grab the proverbial bull by the horns before it knocks you down. If current Big Data systems are finding it challenging to adapt to the rising volume and velocity of Big Data today, then it’s time to re-assess their relevance and root out outdated, inefficient systems.

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